Background subtraction through multiple life span modeling
Junliang Xing, Liwei Liu, Haizhou Ai · 2011
Background subtraction plays a key role in many surveillance systems. A good background subtractor should not only be able to robustly detect targets under different situations (e.g. moving and static), but also to adaptively maintain the background model against various influences (e.g. dynamic scenes and noises). This paper proposes a novel background modeling approach with these good characteristics. By introducing the “life span” concept into a background model, different properties of the scene are obtained through different life span models. Specifically, three different models, i.e., the Long Life Span Model, the Middle Life Span Model, and the Short Life Span Model, are online adaptively built and updated in a collaborative manner. Output of the system gives an adaptive, robust, and efficient estimation of the foreground region which can facility many practical applications. Experiment results on lots of surveillance videos demonstrate the superiority of the proposed method over competing approaches.